The present invention relates in general to real-time fault diagnostic systems, and more particularly to an automatic test generation method for ensuring the operational correctness and resolution of a fault model in a real-time diagnostic system.
In complex industrial processes, a computerized fault diagnostic system is frequently used to monitor alarms and detect possible sources of failure in the industrial process. Real-time fault diagnostic systems observe the operation of processes, detect the appearance and propagation of faults, and continuously update the list of possible fault causes to support the on-line decision making which determines whether to intervene in the process being monitored.
The ultimate purpose of the diagnostic system is to minimize the cost of operation of the industrial process being monitored by finding all possible sources of detected process anomalies as early as possible, and by predicting the prospective adverse economic effect which can be caused by the faults on the operation of related process components. These techniques are particularly applicable in chemical and power engineering because of the extreme expense of down-time and the adverse economic effect which can be caused by a degradation of product quality.
Thus, a diagnostic system is frequently used to monitor extremely complex industrial operations, such as in a chemical or power plant. A typical industrial operation can have thousands of components performing hundreds of operations at any given time. Many of these operations are interdependent, and constantly interact with each other. The failure of any one component can potentially affect adversely the performance of other operations that do not directly use the failed component. Therefore, a single component fault can effectively propagate to many other operations, and set off many different alarms.
Previous systems employ both symptom-based and model-based categories of real-time diagnostic methods. Symptom-based diagnostic methods collect failure symptoms and try to match them with a particular symptom pattern which is characteristic of a possible failure cause. The symptom-failure cause association may be found using pattern recognition methods, deterministic reasoning, or probabilistic reasoning. The main disadvantages of the symptom-based diagnostic methods are that the association is highly dependent upon operational conditions, and that a reliable diagnosis requires the presence of well-developed symptoms, which are not tolerable in most industrial applications. These disadvantages are caused in symptom-based diagnostic systems in part because the number of possible symptoms that are generated by different failure modes can be prohibitively large.
Model-based methods provide much better performance than symptom-based methods, but can only be used when detailed information about the structure of the system being monitored is available. In model-based methods, a model of the industrial process is generated prior to operation of the system, and is used during the diagnostic process to locate the possible failure sources. Different types of models, including quantitative models, qualitative models, and graph models, can be used in the diagnostics. The application of graph models in large scale systems has been most promising, mainly because of the predictable computation load of the applied graph algorithms.
Many model-based diagnostic methods are known in the prior art. However, the results of the diagnosis in terms of accuracy and resolution are determined in part by the accuracy and resolution of the model itself. A correct and more detailed model will lead to better results than will be obtained by a model that contains errors and that is not as detailed.
It therefore an object of the present invention to test the accuracy and completeness of failure propagation model for use in a diagnostic system. It also an object of the present invention to test a failure propagation model by simulating fault scenarios for a component or set of components in the process to be monitored.